𝕄aTOS

Sadaf Abdul Rauf, François Yvon (2024). Translating scientific abstracts in the bio-medical domain with structure-aware models. Computer Speech & Language, vol. 87.

Machine Translation (MT) technologies have improved in many ways and generate usable outputs for a growing number of domains and language pairs. Yet, most sentence based MT systems struggle with contextual dependencies, processing small chunks of texts, typically sentences, in isolation from their textual context. This is likely to cause systematic errors or inconsistencies when processing long documents. While various attempts are made to handle extended contexts in translation, the relevance of these contextual cues, especially those related to the structural organization, and the extent to which they affect translation quality remains an under explored area. In this work, we explore ways to take these structural aspects into account, by integrating document structure as an extra conditioning context. Our experiments on biomedical abstracts, which are usually structured in a rigid way, suggest that this type of structural information can be useful for MT and document structure prediction. We also present in detail the impact of structural information on MT output and assess the degree to which structural information can be learned from the data.
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Ziqian Peng (2023). Document-level Machine Translation for scientific texts. Mémoire de Master, Université Paris-Saclay.

While neural machine translation has seen significant progress during recent years at sentencelevel, translating full documents remains a challenge to efficiently incorporate document-level context. Various approaches have been proposed, but most of them consider only one to three previous source and/or target sentences as the context. This is not sufficient to faithfully translate some language phenomena, like lexical consistency and document coherence, especially in some scientific texts. In this work, we conducted experiments to include full contextual context and investigate the impact of all the past / future sentences on the source side with a context ablation study, on some abstracts from scientific publications. Our results show that future context is more influential than the past source context, and in our experiments, the Transformer architecture performs much better to translate the beginning of a long document than the end.
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Maud Bénard, Alexandra Mestivier, Natalie Kübler, Lichao Zhu, Rachel Bawden, Éric De La Clergerie, Laurent Romary, Mathilde Huguin, Jean-François Nominé, Ziqian Peng, François Yvon (2023). MaTOS Traduction automatique pour la science ouverte. Actes de l'Atelier sur l'Analyse et la Recherche de Textes Scientifiques, CORIA-TALN 2023. 5 juin 2023 Paris (France).

Cette contribution présente le projet MaTOS (Machine Translation for Open Science), qui vise à développer de nouvelles méthodes pour la traduction automatique (TA) intégrale de documents scientifiques entre le français et l’anglais, ainsi que des métriques automatiques pour évaluer la qualité des traductions produites. Pour ce faire, MaTOS s’intéresse (a) au recueil de ressources ouvertes pour la TA spécialisée; (b) à la description des marqueurs de cohérence textuelle pour les articles scientifiques; (c) au développement de nouvelles méthodes de traitement multilingue pour les documents; (d) aux métriques mesurant les progrès de la traduction de documents complets.
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